System and method for determining object intention through visual attributes

ABSTRACT

Systems and methods for determining object intentions through visual attributes are provided. A method can include determining, by a computing system, one or more regions of interest. The regions of interest can be associated with surrounding environment of a first vehicle. The method can include determining, by a computing system, spatial features and temporal features associated with the regions of interest. The spatial features can be indicative of a vehicle orientation associated with a vehicle of interest. The temporal features can be indicative of a semantic state associated with signal lights of the vehicle of interest. The method can include determining, by the computing system, a vehicle intention. The vehicle intention can be based on the spatial and temporal features. The method can include initiating, by the computing system, an action. The action can be based on the vehicle intention.

PRIORITY CLAIM

The present application is based on and claims benefit of U.S.Provisional Application 62/685,714 having a filing date of Jun. 15, 2018and U.S. Provisional Application 62/754,942 having a filing date of Nov.2, 2018, both of which are incorporated by reference herein.

FIELD

The present disclosure relates generally to controlling vehicles. Inparticular, a vehicle can be controlled to determine object intentionsthrough visual attributes.

BACKGROUND

An autonomous vehicle can be capable of sensing its environment andnavigating with little to no human input. In particular, an autonomousvehicle can observe its surrounding environment using a variety ofsensors and can attempt to comprehend the environment by performingvarious processing techniques on data collected by the sensors. Givenknowledge of its surrounding environment, the autonomous vehicle cannavigate through such surrounding environment.

SUMMARY

Aspects and advantages of embodiments of the present disclosure will beset forth in part in the following description, or may be learned fromthe description, or may be learned through practice of the embodiments.

One example aspect of the present disclosure is directed to acomputer-implemented method of determining semantic vehicle intentions.The method includes obtaining, by a computing system including one ormore computing devices, sensor data associated with a surroundingenvironment of a first vehicle. The sensor data includes a sequence ofimage frames, each image frame corresponding to one of a plurality oftime steps. The method includes determining, by the computing system,one or more regions of interest associated with the sensor data. Themethod includes determining, by the computing system, one or morespatial features associated with at least one of the one or more regionsof interest. At least one of the one or more spatial features areindicative of a vehicle orientation associated with a vehicle ofinterest. The method includes determining, by the computing system, oneor more temporal features associated with at least one of the one ormore regions of interest. The one or more temporal features areindicative of one or more semantic states associated with at least onesignal light of the vehicle of interest. The method includesdetermining, by the computing system, an intention associated with thevehicle of interest based, at least in part, on the one or more spatialfeatures and the one or more temporal features. The method includesinitiating, by the computing system, one or more actions based, at leastin part, on the intention.

Another example aspect of the present disclosure is directed to acomputing system including one or more processors and one or moretangible, non-transitory, computer readable media that collectivelystore instructions that when executed by the one or more processorscause the computing system to perform operations. The operations includeobtaining sensor data associated with a surrounding environment of afirst vehicle. The operations include determining, via one or moremachine learning models, one or more regions of interest associated withthe sensor data. The operations include determining, via one or moremachine learning models, one or more spatial features associated with atleast one of the one or more regions of interest. At least one of theone or more spatial features are indicative of an object orientationassociated with an object of interest. The operations includedetermining, via one or more machine learning models, one or moretemporal features associated with at least one of the one or moreregions of interest. The one or more temporal features are indicative ofone or more semantic states associated with at least one signal of theobject of interest. The operations include determining, via one or moremachine learning models, an intention associated with the object ofinterest based, at least in part, on the one or more spatial featuresand the one or more temporal features. The operations include initiatingone or more actions based, at least in part, on the intention.

Yet another aspect of the present disclosure is directed to anautonomous vehicle. The autonomous vehicle includes one or more vehiclesensors, one or more processors, and one or more tangible,non-transitory, computer readable media that collectively storeinstructions that when executed by the one or more processors cause theone or more processors to perform operations. The operations includeobtaining, via the one or more vehicle sensors, sensor data associatedwith a surrounding environment of the autonomous vehicle. The sensordata includes a sequence of image frames at each of a plurality of timesteps. The operations include determining a region of interestassociated with the sensor data. The operations include determining oneor more spatial features associated with the one or more region ofinterest. The operations include determining one or more temporalfeatures associated with the region of interest. The operations includedetermining an intention associated with a vehicle of interest based, atleast in part, on the one or more spatial features and the one or moretemporal features. The operations include initiating one or more actionsbased, at least in part, on the intention.

Other example aspects of the present disclosure are directed to systems,methods, vehicles, apparatuses, tangible, non-transitorycomputer-readable media, and memory devices for controlling autonomousvehicles.

The autonomous vehicle technology described herein can help improve thesafety of passengers of an autonomous vehicle, improve the safety of thesurroundings of the autonomous vehicle, improve the experience of therider and/or operator of the autonomous vehicle, as well as provideother improvements as described herein. Moreover, the autonomous vehicletechnology of the present disclosure can help improve the ability of anautonomous vehicle to effectively provide vehicle services to others andsupport the various members of the community in which the autonomousvehicle is operating, including persons with reduced mobility and/orpersons that are underserved by other transportation options.Additionally, the autonomous vehicle of the present disclosure mayreduce traffic congestion in communities as well as provide alternateforms of transportation that may provide environmental benefits.

These and other features, aspects and advantages of various embodimentswill become better understood with reference to the followingdescription and appended claims. The accompanying drawings, which areincorporated in and constitute a part of this specification, illustrateembodiments of the present disclosure and, together with thedescription, serve to explain the related principles.

BRIEF DESCRIPTION OF THE DRAWINGS

Detailed discussion of embodiments directed to one of ordinary skill inthe art are set forth in the specification, which makes reference to theappended figures, in which:

FIG. 1 depicts an example system overview according to exampleimplementations of the present disclosure;

FIG. 2 depicts an example data flow diagram of an example intentionsystem according to example implementations of the present disclosure;

FIG. 3 depicts an example region of interest according to exampleimplementations of the present disclosure;

FIG. 4 depicts an example model architecture according to exampleimplementations of the present disclosure;

FIG. 5 depicts an example flow diagram of an example method fordetermining semantic object intentions according to exampleimplementations of the present disclosure;

FIG. 6 depicts an example system with units for performing operationsand functions according to example implementations of the presentdisclosure; and

FIG. 7 depicts example system components of an example system accordingto example implementations of the present disclosure.

DETAILED DESCRIPTION

Reference now will be made in detail to embodiments, one or moreexample(s) of which are illustrated in the drawings. Each example isprovided by way of explanation of the embodiments, not limitation of thepresent disclosure. In fact, it will be apparent to those skilled in theart that various modifications and variations can be made to theembodiments without departing from the scope or spirit of the presentdisclosure. For instance, features illustrated or described as part ofone embodiment can be used with another embodiment to yield a stillfurther embodiment. Thus, it is intended that aspects of the presentdisclosure cover such modifications and variations.

The present disclosure is directed to improved systems and methods fordetermining vehicle intention through visual attributes. For example,vehicle intention can be communicated by signal lights and can include afuture left turn, right turn, and/or an emergency. For safe operation,it is important for vehicles to reliably determine the intention ofother vehicles, as communicated by signal lights, within theirsurrounding environment. Accurate predictions of vehicle intention canhelp guide a vehicle's detection and tracking of other trafficparticipants as well as the planning of an autonomous vehicle's motion.

The systems and methods of the present disclosure provide an improvedapproach for determining the intention of vehicles within thesurrounding environment of a first vehicle based on various featuresextracted from sensor data. For instance, the first vehicle can obtainsensor data (e.g., camera image data) via its onboard cameras. Thesensor data can depict one or more signal lights within the surroundingenvironment of the first vehicle. The first vehicle can pre-process thesensor data to generate one or more regions of interest that include thesignal light(s). The systems and methods of the present disclosure cananalyze (e.g., via one or more machine learned models) the one or moreregions of interest to determine one or more spatial features (e.g.,vehicle model, vehicle orientation, occluding objects, etc.) and one ormore temporal features (e.g., states of the one or more signal lightsover time, etc.) associated with each of the region(s) of interest. Thespatial and temporal feature(s) can be feed into a machine learnedobject intention model, which can be trained to accurately determineobject intention based on the spatial and temporal features associatedwith each of the one or more regions of interest. The determined objectintention can be indicative of, for example, whether a proximate vehiclemay intend to turn left, right, stop, etc. The first vehicle can utilizethe determined object intention to improve its performance of variousactions such as, for example, predicting the motion of proximate objects(e.g., vehicles, bicycles, etc.) or, if the first vehicle is anautonomous vehicle, planning vehicle motion according to the predictedmotion of proximate objects.

In some implementations, a first vehicle can include an autonomousvehicle. An autonomous vehicle (e.g., ground-based vehicle, etc.) caninclude various systems and devices configured to control the operationof the vehicle. For example, an autonomous vehicle can include anonboard vehicle computing system (e.g., located on or within theautonomous vehicle) that is configured to operate the autonomousvehicle. The vehicle computing system can obtain sensor data fromsensor(s) onboard the vehicle (e.g., cameras, LIDAR, RADAR, etc.),attempt to comprehend the vehicle's surrounding environment byperforming various processing techniques on the sensor data, andgenerate an appropriate motion plan through the vehicle's surroundingenvironment. For example, the sensor data can be used in a processingpipeline that includes the detection of objects proximate to theautonomous vehicle (e.g., within the field of view of vehicle sensors),object motion prediction, and vehicle motion planning. For example, amotion plan can be determined by the vehicle computing system based on adetermined object intention, and the vehicle can be controlled by avehicle controller to initiate travel in accordance with the motionplan. The autonomous vehicle can also include one or more output devicessuch as, for example, one or more display screens (e.g., touch-sensitiveinteractive display screens), speakers, or other devices configured toprovide informational prompts to a vehicle operator.

In some implementations, the first vehicle can include a non-autonomousvehicle. For instance, any vehicle may utilize the technology describedherein for determining object intention. For example, a non-autonomousvehicle may utilize aspects of the present disclosure to determine theintention of one or more objects (e.g., vehicles, bicycles, etc.)proximate to a non-autonomous vehicle. Such information may be utilizedby a non-autonomous vehicle, for example, to provide informationalnotifications to an operator of the non-autonomous vehicle. Forinstance, the non-autonomous vehicle can notify or otherwise warn theoperator of the non-autonomous vehicle based on a determined objectintention.

To facilitate the determination of an object intention associated withan object of interest (e.g., a vehicle proximate to a first vehicle) anintention system can obtain sensor data. The sensor data can include anydata associated with the surrounding environment of a first vehicle suchas, for example, camera image data and/or Light Detection and Ranging(LIDAR) data. For example, in some implementations, the sensor data caninclude a sequence of image frames at each of a plurality of time steps.In such an implementation, the sequence of image frames can be capturedin forward-facing video on one or more platforms of the first vehicle.

The sensor data can be associated with a surrounding environment of afirst vehicle. Moreover, the sensor data can include one or more objectsof interest within the surrounding environment of the first vehicle. Theone or more objects of interest can include any moveable object within athreshold distance from the first vehicle. In some implementations, thethreshold distance can include a predetermined distance. Additionally,or alternatively, the intention system can dynamically determine thethreshold distance based on one or more factors such as weather, roadwayconditions, environment, etc. For example, the one or more factors canindicate a potentially hazardous situation (e.g., heavy rain,construction, etc.). In such a case, the intention system can determinea larger threshold distance enhance roadway safety.

In some implementations, the one or more objects of interest can includeone or more vehicles of interest. The one or more vehicles of interestcan include, for example, any motorized object (e.g., motorcycles,automobiles, etc.). The one or more vehicles of interest (e.g.,autonomous vehicles, non-autonomous vehicles, etc.) can be equipped withspecific hardware to facilitate intent-related communication. Forexample, the one or more vehicles of interest can include one or moresignal lights (e.g., turn signals, hazard lights, etc.) to signal thevehicle's intention. The vehicles intention, for example, can includefuture actions such as lane changes, parking, and/or one or more turns.For instance, a vehicle can signal its intention to stay in a parkedposition by simultaneously toggling two turn signals on/off in ablinking pattern (e.g., by turning on its hazard lights). In otherscenarios, a vehicle can signal its intention to turn by toggling a turnsignal on/off.

In some implementations, the intention system can analyze the sensordata to determine one or more regions of interest. For example, theintention system can process one or more image frames of the sensor datausing one or more machine learning techniques. For instance, in someimplementations, the intention system can apply a spatial mask and afully convolutional network to extract the one or more regions ofinterest from the sensor data.

The one or more regions of interest can include one or more croppedimage frames associated with an object of interest. For instance, eachregion of interest can include at least one vehicle of interest. Moreparticularly, the one or more cropped image frames can include anaxis-aligned region of interest around each vehicle of interest.Moreover, in some implementations, each region of interest can includethe signal light(s) associated with a vehicle of interest. In someimplementations, each region of interest can include one or more statesassociated with the signal light(s). For instance, each of the signallight(s) can be illuminated or not depending on a time associated withthe region of interest. In this manner, the region(s) of interest caninclude a streaming input of cropped image frames providing informationassociated with a vehicle of interest over time.

The intention system can determine one or more spatial featuresassociated with at least one of the region(s) of interest. For example,in some implementations, the intention system can provide the one ormore regions of interest as input to one or more machine learned models.For instance, at least one of the machine learned model(s) can include aconvolutional neural network (e.g., a VGG16 based convolutional neuralnetwork) and/or another type of model. The machine learned model(s) canthereby extract one or more spatial features associated with the regionsof interest.

In some implementations, the one or more spatial features can include amodel representation of the vehicle of interest. For example, the modelrepresentation of the vehicle of interest can include one or morephysical characteristics associated with the vehicle of interest. Theone or more physical characteristics can include information associatedwith the vehicle of interest such as, for example, vehicle type,position, orientation, etc. For example, in some implementations, themodel of the vehicle of interest can identify a vehicle orientationassociated with the vehicle of interest. The vehicle orientation can bedetermined relative to another object within the surrounding environmentof the first vehicle. For example, the vehicle orientation can bedetermined relative to one or more lane boundaries, a traffic light, asign post such as a stop sign, a second vehicle within the firstvehicles surrounding environment, etc. In some implementations, thevehicle orientation can be relative to the first vehicle. For example,the vehicle orientation can be based on the direction from which thevehicle of interest is viewed from the first vehicle. By way of example,the vehicle orientation can include designations such as behind, left,front, and/or right. In such an example, each designation can identifythe direction from which the vehicle of interest is viewed from thefirst vehicle.

Additionally, or alternatively, the one or more spatial features caninclude one or more occluding objects. The one or more occluding objectscan include any object within a region of interest other than the objectof interest (e.g., vehicle of interest). For example, the one or moreoccluding objects can include one or more objects disrupting the view ofa vehicle of interest. More particularly, the one or more occludingobjects can include, for example, one or more objects disrupting theview of at least one signal light (e.g., one or more headlights,taillights, etc.) of the vehicle of interest. For instance, theoccluding object(s) can be positioned between the vehicle of interestand one or more sensor(s) onboard the first vehicle. For example, theoccluding object can be positioned in such a way (e.g., within thesensor's field of view) as to at least partially block the sensor(s)from capturing sensor data associated with the vehicle of interest(e.g., one or more turn signals of the vehicle of interest).

The intention system can determine one or more temporal featuresassociated with at least one of the region(s) of interest. For example,in some implementations, the intention system can provide a dataindicative of the region(s) of interest at multiple time steps as inputto one or more machine learned models. For instance, at least onemachine learned model can include a convolutional neural network (e.g.,convolutional LSTM). The machine learned model can extract one or moretemporal features associated with the regions of interest.

The one or more temporal features can include temporal characteristicsof the region(s) of interest (e.g., a streaming input of image data).For example, the temporal feature(s) can include one or more semanticstates associated with at least one signal light of a vehicle ofinterest over time. For instance, the one or more semantic states caninclude designations such as “off,” “on,” and “unknown.” By way ofexample, “off” can indicate that a signal light did not illuminate overa time period; “on” can indicate that the signal light illuminated insome manner over a time period; and “unknown” can indicate the presenceof an occluding object over a time period. In this manner, the temporalfeature(s) can distinguish flashing lights and persistent lights fromother specious light patterns.

The intention system can utilize a variety of machine learned modelconfigurations to determine the one or more temporal features and theone or more spatial features. For example, in some implementations, thesame machine learned model can be trained to determine the one or moretemporal features and the one or more spatial features. Additionally, oralternatively, the temporal features can be determined separately fromthe one or more spatial features. For example, the temporal features andspatial features can be determined by different machine learned models.For instance, the temporal features can be determined via a firstmachine learned model (e.g., a convolutional LSTM), while the spatialfeatures can be determined by a second machine learned model (e.g., aconvolutional neural network).

Moreover, the temporal features and the spatial features can bedetermined concurrently and/or sequentially. For instance, the intentionsystem can input the one or more regions of interest into two machinelearned models to concurrently determine the one or more temporalfeatures and the one or more spatial features. In some implementations,the intention system can determine the spatial feature(s) and thetemporal feature(s) in a predetermined order. For example, the intentionsystem can first input the one or more regions of interest into amachine learned model to determine the one or more spatial features andsubsequently input the one or more regions of interest into the same ora different machine learned model to determine the one or more temporalfeatures. In some implementations, the intention system can firstdetermine the one or more temporal features and subsequently determinethe one or more spatial features.

The intention system can determine an object intention associated withthe object of interest. For instance, the intention system can determineobject intention (e.g., vehicle intention) associated with a vehicle ofinterest. For example, the vehicle intention can indicate a predictedmovement of the vehicle of interest such as a future left turn, rightturn, emergency (e.g., flashers), and/or unknown. For example, in someimplementations, the intention system can provide one or more temporalfeatures and one or more spatial features to one or more machine learnedmodels. The one or more machine learned models can include the same ordifferent machine learned models that are used to determine the spatialfeature(s) and/or temporal feature(s). In some implementations, at leastone of these machine learned model(s) can include a fully connectedneural network. In this instance, the features can be passed through thefully connected layer to produce one or more variables of interest suchas vehicle intention.

In this manner, the object intention can be based, at least in part, onthe spatial feature(s) and/or temporal feature(s). For example, thevehicle intention can be determined based, at least in part, on the oneor more semantic states associated with at least one signal light of thevehicle of interest. For instance, a semantic state of “on” associatedwith a right turn signal and a semantic state of “off” associated with aleft turn signal can indicate a right turn.

Moreover, in some implementations, the object intention can bedetermined based, at least in part, on the orientation of the object ofinterest. For instance, in the example scenario above regarding theright turn, the intention system can instead determine a left turndepending on the orientation of the vehicle. By way of example, thecorrect vehicle intention is a right turn when the vehicle of interestis being viewed from behind. Otherwise, for example if the vehicle ofinterest is being viewed from the front, the correct vehicle intentioncan be a left turn (e.g., the turn signal on the right side of thevehicle of interest identifies a left turn rather than a right turn).Thus, by accounting for the orientation of the object of interest, theintention system can improve the accuracy of object intention bydetermining a correct left turn rather than a right turn.

The intention system can initiate one or more actions based, at least inpart, on the object intention. The one or more action(s) can include,for example, planning safe maneuvers, issuing one or more informationalprompts, etc. For example, a bus (e.g., a vehicle of interest) cansignal its intention to make a stop to pick up and drop off passengersby turning on one or more signal lights (e.g., emergency flashers). Insuch a case, the first vehicle can initiate one or more actions based onthe bus's intention to stop. For example, in the event that the firstvehicle is an autonomous vehicle, the intention system can provide dataindicative of the bus's intention to stop to the vehicle's autonomysystem (or sub-systems) such that the first vehicle can generate one ormore motion plans to avoid the stopped bus (e.g., changing lanes,decelerating, etc.). Moreover, the first vehicle can initiate theidentified motion plan (e.g., to safely avoid any interference with thebus). Additionally, or alternatively, the intention system can prompt anoperator of the first vehicle. For instance, the first vehicle can issuea warning associated with the bus's intended stop and/or present arecommended maneuver to the operator of the first vehicle. In thismanner, the intention system can reduce delays and congestions on theroadways by accounting for future actions of objects within the firstvehicles surrounding environment, while also increasing the safety ofthe objects and first vehicle.

As another example, a truck (and/or the operator thereof) may intend tochange lanes such that the truck will be in front of the first vehicle.Beforehand, the truck can signal its intention by activating one or moreof the truck's signal lights (e.g., a right turn signal). In such acase, the intention system can determine the truck's intention andinitiate one or more actions. For example, the intention system canidentify one or more motion plans to avoid the truck (e.g., bydecreasing its speed). Moreover, the intention system can issue awarning and/or present a recommended maneuver to the operator of thefirst vehicle. In some implementations, where the first vehicle is anautonomous vehicle, the first vehicle can plan and initiate theidentified motion plan. In this manner, the intention system can furtherreduce delays and congestions on the road caused, for example, byvarious movements of the objects within the first vehicle'ssurroundings.

Although the above description provides examples that discuss vehiclesof interest, the intention system is not limited to vehicles and can beapplied to any object within the first vehicle's surroundingenvironment. For example, in some implementations, the intention systemcan be configured to determine the intention of one or more objects(e.g., bicycles) within the surrounding environment of the firstvehicle. For example, the one or more regions of interest can includeone or more bicycles of interest. More particularly, the regions ofinterest can include one or more signals associated with the object ofinterest (e.g., the bicycles of interest). Moreover, the intentionsystem can be configured to determine one or more spatial features andone or more temporal features associated with a region of interestaround the bicycle of interest (e.g., using machine learned model(s)that have been trained to analyze signals associated with a bicycle)Based on the spatial feature(s) and temporal feature(s), the intentionsystem can determine a bicycle intention associated with the bicycle(e.g., using the trained model(s)) and initiate an action accordingly(e.g., output data for autonomous vehicle operation, provide data fordisplay to an operator via a display device, etc.).

The systems and methods described herein provide a number of technicaleffects and benefits. For instance, the present disclosure allows avehicle to more accurately predict object intention by using a series ofimproved models (e.g., neural network models, etc.) capable ofleveraging sensor data (e.g., including temporal image sequences) toaccurately decipher communications such as turn signals. Such anapproach can allow for improved motion prediction of proximate objectsand autonomous vehicle motion planning. Moreover, the systems andmethods of the present disclosure provide a holistic vehicle intentionformulation capable of estimating turn signals even when the visualevidence is small, and occlusions are frequent. The intention models ofthe present disclosure allow for accurate reasoning about objectintention in situations where the signal(s) of an object are misleading.This provides for more accurate object intention predictions, forexample, when the orientation of an object affects the intended meaningof a signal (e.g., the viewing direction of a vehicle effects theintended meaning of a turn signal). Such an approach can provide a morereliable, flexible, and scalable solution than models with handcraftedrules, especially in less ideal scenarios where heavy occlusion or theorientation of an object may otherwise affect the characterization of asignal. In this way, the present disclosure enhances the operation of avehicle (e.g., autonomous vehicles, etc.) by improving the ability ofthe vehicle to determine the intention of other surrounding objects,while also improving the ability of an autonomous vehicle to plan andcontrol its motion accordingly.

Example aspects of the present disclosure can provide an improvement tovehicle computing technology, such as autonomous vehicle computingtechnology. For instance, the systems and methods of the presentdisclosure allow vehicle technology to leverage sensor data acquired bya first vehicle to more accurately determine the intention of vehiclesproximate to the first vehicle. For example, a computing system (e.g.,vehicle computing system) can obtain sensor data associated with asurrounding environment of a first vehicle. The computing system candetermine one or more regions of interest associated with the sensordata. For example, the sensor data can include a sequence of videoframes from each of a plurality of time steps. The computing system candetermine one or more spatial features (e.g., vehicle orientation)associated with at least one of the one or more regions of interest. Thecomputing system can determine one or more temporal features (e.g.,semantic states of signal lights) associated with at least one of theone or more regions of interest. The computing system can determine avehicle intention associated with a vehicle of interest based, at leastin part, on the one or more spatial features and the one or moretemporal features. The computing system can initiate one or more actionsbased, at least in part, on the vehicle intention. Given the largeintra-class variations with signal lights, frequent occlusions, andsmall visual evidence, hard coded premises of how turn signals shouldbehave cannot account for the diversity of driving scenarios that areencountered every day. However, by leveraging a differentiable systemthat can be trained end-to-end using modern deep learning techniques,the systems and methods of the present disclosure can avoid the pitfallsof relying upon such hard-coded premises. Moreover, the systems andmethods of the present disclosure can combine the strength of twodistinct types of features (e.g., spatial and temporal) associated withsensor data to provide a significant improvement (e.g., 10-30% increasein accuracy) over other turn signal detection approaches. In thismanner, the technology of the present disclosure achieves improved,accurate turn signal detection as a solution to a prevailing problem ofaccurate signal detection. Ultimately, the present disclosure utilizesspecific machine learning techniques and holistic data to achievenumerous benefits (e.g., accurate vehicle intention predictionsregardless of the orientation of a vehicle), that previous, inferiorsignal detection techniques fail to achieve.

With reference now to the FIGS., example aspects of the presentdisclosure will be discussed in further detail. FIG. 1 illustrates anexample vehicle computing system 100 according to example embodiments ofthe present disclosure. The vehicle computing system 100 can beassociated with a vehicle 105. The vehicle computing system 100 can belocated onboard (e.g., included on and/or within) the vehicle 105.

The vehicle 105 incorporating the vehicle computing system 100 can bevarious types of vehicles. The vehicle 105 can be an autonomous vehicle.For instance, the vehicle 105 can be a ground-based autonomous vehiclesuch as an autonomous car, autonomous truck, autonomous bus, scooter,bike, other form factors, etc. The vehicle 105 can be an air-basedautonomous vehicle (e.g., airplane, helicopter, or other aircraft) orother types of vehicles (e.g., watercraft, etc.). The vehicle 105 candrive, navigate, operate, etc. with minimal and/or no interaction from ahuman operator 106 (e.g., driver). An operator 106 (also referred to asa vehicle operator) can be included in the vehicle 105 and/or remotefrom the vehicle 105. In some implementations, the vehicle 105 can be anon-autonomous vehicle.

In some implementations, the vehicle 105 can be configured to operate ina plurality of operating modes. The vehicle 105 can be configured tooperate in a fully autonomous (e.g., self-driving) operating mode inwhich the vehicle 105 is controllable without user input (e.g., candrive and navigate with no input from a vehicle operator present in thevehicle 105 and/or remote from the vehicle 105). The vehicle 105 canoperate in a semi-autonomous operating mode in which the vehicle 105 canoperate with some input from a vehicle operator present in the vehicle105 (and/or a human operator that is remote from the vehicle 105). Thevehicle 105 can enter into a manual operating mode in which the vehicle105 is fully controllable by a vehicle operator 106 (e.g., human driver,pilot, etc.) and can be prohibited and/or disabled (e.g., temporary,permanently, etc.) from performing autonomous navigation (e.g.,autonomous driving). In some implementations, the vehicle 105 canimplement vehicle operating assistance technology (e.g., collisionmitigation system, power assist steering, etc.) while in the manualoperating mode to help assist the vehicle operator of the vehicle 105.For example, a collision mitigation system can utilize a predictedintention of objects within the vehicle's 105 surrounding environment toassist an operator 106 in avoiding collisions and/or delays even when inmanual mode.

The operating modes of the vehicle 105 can be stored in a memory onboardthe vehicle 105. For example, the operating modes can be defined by anoperating mode data structure (e.g., rule, list, table, etc.) thatindicates one or more operating parameters for the vehicle 105, while inthe particular operating mode. For example, an operating mode datastructure can indicate that the vehicle 105 is to autonomously plan itsmotion when in the fully autonomous operating mode. The vehiclecomputing system 100 can access the memory when implementing anoperating mode.

The operating mode of the vehicle 105 can be adjusted in a variety ofmanners. For example, the operating mode of the vehicle 105 can beselected remotely, off-board the vehicle 105. For example, a remotecomputing system (e.g., of a vehicle provider and/or service entityassociated with the vehicle 105) can communicate data to the vehicle 105instructing the vehicle 105 to enter into, exit from, maintain, etc. anoperating mode. For example, in some implementations, the remotecomputing system can be an operations computing system 195, as disclosedherein. By way of example, such data communicated to a vehicle 105 bythe operations computing system 195 can instruct the vehicle 105 toenter into the fully autonomous operating mode. In some implementations,the operating mode of the vehicle 105 can be set onboard and/or near thevehicle 105. For example, the vehicle computing system 100 canautomatically determine when and where the vehicle 105 is to enter,change, maintain, etc. a particular operating mode (e.g., without userinput). Additionally, or alternatively, the operating mode of thevehicle 105 can be manually selected via one or more interfaces locatedonboard the vehicle 105 (e.g., key switch, button, etc.) and/orassociated with a computing device proximate to the vehicle 105 (e.g., atablet operated by authorized personnel located near the vehicle 105).In some implementations, the operating mode of the vehicle 105 can beadjusted by manipulating a series of interfaces in a particular order tocause the vehicle 105 to enter into a particular operating mode.

The vehicle computing system 100 can include one or more computingdevices located onboard the vehicle 105. For example, the computingdevice(s) can be located on and/or within the vehicle 105. The computingdevice(s) can include various components for performing variousoperations and functions. For instance, the computing device(s) caninclude one or more processors and one or more tangible, non-transitory,computer readable media (e.g., memory devices, etc.). The one or moretangible, non-transitory, computer readable media can store instructionsthat when executed by the one or more processors cause the vehicle 105(e.g., its computing system, one or more processors, etc.) to performoperations and functions, such as those described herein for determiningobject intentions based on physical attributes.

The vehicle 105 can include a communications system 120 configured toallow the vehicle computing system 100 (and its computing device(s)) tocommunicate with other computing devices. The vehicle computing system100 can use the communications system 120 to communicate with one ormore computing device(s) that are remote from the vehicle 105 over oneor more networks (e.g., via one or more wireless signal connections). Insome implementations, the communications system 120 can allowcommunication among one or more of the system(s) on-board the vehicle105. The communications system 120 can include any suitable componentsfor interfacing with one or more network(s), including, for example,transmitters, receivers, ports, controllers, antennas, and/or othersuitable components that can help facilitate communication.

As shown in FIG. 1, the vehicle 105 can include one or more vehiclesensors 125, an autonomy computing system 130, one or more vehiclecontrol systems 135, and other systems, as described herein. One or moreof these systems can be configured to communicate with one another via acommunication channel. The communication channel can include one or moredata buses (e.g., controller area network (CAN)), on-board diagnosticsconnector (e.g., OBD-II), and/or a combination of wired and/or wirelesscommunication links. The onboard systems can send and/or receive data,messages, signals, etc. amongst one another via the communicationchannel.

The vehicle sensor(s) 125 can be configured to acquire sensor data 140.This can include sensor data associated with the surrounding environmentof the vehicle 105. For instance, the sensor data 140 can acquire imageand/or other data within a field of view of one or more of the vehiclesensor(s) 125. The vehicle sensor(s) 125 can include a Light Detectionand Ranging (LIDAR) system, a Radio Detection and Ranging (RADAR)system, one or more cameras (e.g., visible spectrum cameras, infraredcameras, etc.), motion sensors, and/or other types of imaging capturedevices and/or sensors. The sensor data 140 can include image data,radar data, LIDAR data, and/or other data acquired by the vehiclesensor(s) 125. The vehicle 105 can also include other sensors configuredto acquire data associated with the vehicle 105. For example, thevehicle 105 can include inertial measurement unit(s), wheel odometrydevices, and/or other sensors.

In some implementations, the sensor data 140 can be indicative of one ormore objects within the surrounding environment of the vehicle 105. Theobject(s) can include, for example, vehicles, pedestrians, bicycles,and/or other objects. The object(s) can be located in front of, to therear of, to the side of the vehicle 105, etc. The sensor data 140 can beindicative of locations associated with the object(s) within thesurrounding environment of the vehicle 105 at one or more times. Thevehicle sensor(s) 125 can provide the sensor data 140 to the autonomycomputing system 130.

In addition to the sensor data 140, the autonomy computing system 130can retrieve or otherwise obtain map data 145. The map data 145 canprovide information about the surrounding environment of the vehicle105. In some implementations, the vehicle 105 can obtain detailed mapdata that provides information regarding: the identity and location ofdifferent roadways, road segments, buildings, or other items or objects(e.g., lampposts, crosswalks, curbing, etc.); the location anddirections of traffic lanes (e.g., the location and direction of aparking lane, a turning lane, a bicycle lane, or other lanes within aparticular roadway or other travel way and/or one or more boundarymarkings associated therewith); traffic control data (e.g., the locationand instructions of signage, traffic lights, or other traffic controldevices); the location of obstructions (e.g., roadwork, accidents,etc.); data indicative of events (e.g., scheduled concerts, parades,etc.); and/or any other map data that provides information that assiststhe vehicle 105 in comprehending and perceiving its surroundingenvironment and its relationship thereto. In some implementations, thevehicle computing system 100 can determine a vehicle route for thevehicle 105 based at least in part on the map data 145.

The vehicle 105 can include a positioning system 150. The positioningsystem 150 can determine a current position of the vehicle 105. Thepositioning system 150 can be any device or circuitry for analyzing theposition of the vehicle 105. For example, the positioning system 150 candetermine position by using one or more of inertial sensors (e.g.,inertial measurement unit(s), etc.), a satellite positioning system,based on IP address, by using triangulation and/or proximity to networkaccess points or other network components (e.g., cellular towers, WiFiaccess points, etc.) and/or other suitable techniques. The position ofthe vehicle 105 can be used by various systems of the vehicle computingsystem 100 and/or provided to a remote computing system. For example,the map data 145 can provide the vehicle 105 relative positions of theelements of a surrounding environment of the vehicle 105. The vehicle105 can identify its position within the surrounding environment (e.g.,across six axes, etc.) based at least in part on the map data 145. Forexample, the vehicle computing system 100 can process the sensor data140 (e.g., LIDAR data, camera data, etc.) to match it to a map of thesurrounding environment to get an understanding of the vehicle'sposition within that environment.

The autonomy computing system 130 can include a perception system 155, aprediction system 160, a motion planning system 165, and/or othersystems that cooperate to perceive the surrounding environment of thevehicle 105 and determine a motion plan for controlling the motion ofthe vehicle 105 accordingly. For example, the autonomy computing system130 can obtain the sensor data 140 from the vehicle sensor(s) 125,process the sensor data 140 (and/or other data) to perceive itssurrounding environment, predict the motion of objects within thesurrounding environment, and generate an appropriate motion plan throughsuch surrounding environment. The autonomy computing system 130 cancommunicate with the one or more vehicle control systems 135 to operatethe vehicle 105 according to the motion plan.

The vehicle computing system 100 (e.g., the autonomy computing system130) can identify one or more objects that are proximate to the vehicle105 based at least in part on the sensor data 140 and/or the map data145. For example, the vehicle computing system 100 (e.g., the perceptionsystem 155) can process the sensor data 140, the map data 145, etc. toobtain perception data 170. The vehicle computing system 100 cangenerate perception data 170 that is indicative of one or more states(e.g., current and/or past state(s)) of a plurality of objects that arewithin a surrounding environment of the vehicle 105. For example, theperception data 170 for each object can describe (e.g., for a giventime, time period) an estimate of the object's: current and/or pastlocation (also referred to as position); current and/or pastspeed/velocity; current and/or past acceleration; current and/or pastheading; current and/or past orientation; size/footprint (e.g., asrepresented by a bounding shape); class (e.g., pedestrian class vs.vehicle class vs. bicycle class), the uncertainties associatedtherewith, and/or other state information. The perception system 155 canprovide the perception data 170 to the prediction system 160, the motionplanning system 165, the intention system 185, and/or other system(s).

The prediction system 160 can be configured to predict a motion of theobject(s) within the surrounding environment of the vehicle 105. Forinstance, the prediction system 160 can generate prediction data 175associated with such object(s). The prediction data 175 can beindicative of one or more predicted future locations of each respectiveobject. For example, the prediction system 160 can determine a predictedmotion trajectory along which a respective object is predicted to travelover time. A predicted motion trajectory can be indicative of a paththat the object is predicted to traverse and an associated timing withwhich the object is predicted to travel along the path. The predictedpath can include and/or be made up of a plurality of way points. In someimplementations, the prediction data 175 can be indicative of the speedand/or acceleration at which the respective object is predicted totravel along its associated predicted motion trajectory. In someimplementations, the prediction data 175 can include a predicted objectintention (e.g., a right turn) based on physical attributes of theobject. The prediction system 160 can output the prediction data 175(e.g., indicative of one or more of the predicted motion trajectories)to the motion planning system 165.

The vehicle computing system 100 (e.g., the motion planning system 165)can determine a motion plan 180 for the vehicle 105 based at least inpart on the perception data 170, the prediction data 175, and/or otherdata. A motion plan 180 can include vehicle actions (e.g., plannedvehicle trajectories, speed(s), acceleration(s), intention, otheractions, etc.) with respect to one or more of the objects within thesurrounding environment of the vehicle 105 as well as the objects'predicted movements. For instance, the motion planning system 165 canimplement an optimization algorithm, model, etc. that considers costdata associated with a vehicle action as well as other objectivefunctions (e.g., cost functions based on speed limits, traffic lights,etc.), if any, to determine optimized variables that make up the motionplan 180. The motion planning system 165 can determine that the vehicle105 can perform a certain action (e.g., pass an object, etc.) withoutincreasing the potential risk to the vehicle 105 and/or violating anytraffic laws (e.g., speed limits, lane boundaries, signage, etc.). Forinstance, the motion planning system 165 can evaluate one or more of thepredicted motion trajectories of one or more objects during its costdata analysis as it determines an optimized vehicle trajectory throughthe surrounding environment. The motion planning system 165 can generatecost data associated with such trajectories. In some implementations,one or more of the predicted motion trajectories may not ultimatelychange the motion of the vehicle 105 (e.g., due to an overridingfactor). In some implementations, the motion plan 180 may define thevehicle's motion such that the vehicle 105 avoids the object(s), reducesspeed to give more leeway to one or more of the object(s), proceedscautiously, performs a stopping action, etc.

The motion planning system 165 can be configured to continuously updatethe vehicle's motion plan 180 and a corresponding planned vehicle motiontrajectory. For example, in some implementations, the motion planningsystem 165 can generate new motion plan(s) for the vehicle 105 (e.g.,multiple times per second). Each new motion plan can describe a motionof the vehicle 105 over the next planning period (e.g., next severalseconds). Moreover, a new motion plan may include a new planned vehiclemotion trajectory. Thus, in some implementations, the motion planningsystem 165 can continuously operate to revise or otherwise generate ashort-term motion plan based on the currently available data. Once theoptimization planner has identified the optimal motion plan (or someother iterative break occurs), the optimal motion plan (and the plannedmotion trajectory) can be selected and executed by the vehicle 105.

The vehicle computing system 100 can cause the vehicle 105 to initiate amotion control in accordance with at least a portion of the motion plan180. A motion control can be an operation, action, etc. that isassociated with controlling the motion of the vehicle. For instance, themotion plan 180 can be provided to the vehicle control system(s) 135 ofthe vehicle 105. The vehicle control system(s) 135 can be associatedwith a vehicle controller (e.g., including a vehicle interface) that isconfigured to implement the motion plan 180. The vehicle controller can,for example, translate the motion plan into instructions for theappropriate vehicle control component (e.g., acceleration control, brakecontrol, steering control, etc.). By way of example, the vehiclecontroller can translate a determined motion plan 180 into instructionsto adjust the steering of the vehicle 105 “X” degrees, apply a certainmagnitude of braking force, etc. The vehicle controller (e.g., thevehicle interface) can help facilitate the responsible vehicle control(e.g., braking control system, steering control system, accelerationcontrol system, etc.) to execute the instructions and implement themotion plan 180 (e.g., by sending control signal(s), making thetranslated plan available, etc.). This can allow the vehicle 105 toautonomously travel within the vehicle's surrounding environment.

As shown in FIG. 1, the vehicle 105 can include an HMI (“Human MachineInterface”) 190 that can output data and accept input from the operator106 of the vehicle 105. For instance, the HMI 190 can include one ormore output devices (e.g., speakers, display devices, tactile devices,etc.) such that, in some implementations, the HMI 190 can provide one ormore informational prompts to the operator 106 of the vehicle 105. Forexample, the HMI 190 can be configured to provide prediction data 170such as a predicted object intention to one or more vehicle operator(s)106. Additionally, or alternatively, the HMI 190 can include one or moreinput devices (e.g., buttons, microphones, cameras, etc.) to acceptvehicle operator 106 input. In this manner, the HMI 190 can communicatewith the vehicle operator 106.

The vehicle computing system 100 can include an intention system 185. Asillustrated in FIG. 1 the intention system 185 can be implementedonboard the vehicle 105 (e.g., as a portion of the vehicle computingsystem 100). Moreover, in some implementations, the intention system 185can be remote from the vehicle 105 (e.g., as a portion of an operationscomputing system 195). The intention system 185 can determine one ormore object intention(s) associated with objects within the surroundingenvironment of the vehicle 105, as described in greater detail herein.In some implementations, the intention system 185 can be configured tooperate in conjunction with the vehicle autonomy system 130. Forexample, the intention system 185 can send data to and receive data fromthe vehicle autonomy system 130. In some implementations, the intentionsystem 185 can be included in or otherwise a part of a vehicle autonomysystem 130. The intention system 185 can include software and hardwareconfigured to provide the functionality described herein. In someimplementations, the intention system 185 can be implemented as asubsystem of a vehicle computing system 100. Additionally, oralternatively, the intention system 185 can be implemented via one ormore computing devices that are remote from the vehicle 105. Exampleintention system 185 configurations according to example aspects of thepresent disclosure are discussed in greater detail with respect to FIGS.2-6.

The operator 106 can be associated with the vehicle 105 to take manualcontrol of the vehicle, if necessary. For instance, in a testingscenario, a vehicle 105 can be periodically tested with controlledfaults that can be injected into an autonomous vehicle's autonomy system130. This can help the vehicle's response to certain scenarios. Avehicle operator 106 can be located within the vehicle 105 and/or remotefrom the vehicle 105 to take control of the vehicle 105 (e.g., in theevent the fault results in the vehicle exiting from a fully autonomousmode in the testing environment).

Although many examples are described herein with respect to autonomousvehicles, the disclosed technology is not limited to autonomousvehicles. For instance, any vehicle may utilize the technology describedherein for determining object intention. For example, a non-autonomousvehicle may utilize aspects of the present disclosure to determine theintention of one or more objects (e.g., vehicles, bicycles, etc.)proximate to a non-autonomous vehicle. Such information may be utilizedby a non-autonomous vehicle, for example, to provide informationalnotifications to an operator of the non-autonomous vehicle. Forinstance, the non-autonomous vehicle can notify or otherwise warn theoperator of the non-autonomous vehicle based on a determined objectintention.

FIG. 2 depicts an example data flow diagram 200 of an example intentionsystem 185 according to example implementations of the presentdisclosure. To facilitate the determination of an object intentionassociated with an object of interest (e.g., a vehicle proximate to afirst vehicle) the intention system 185 can obtain sensor data 140 vianetwork 205. As described above with reference to FIG. 1, sensor data140 can include any data associated with the surrounding environment ofthe vehicle 105 such as, for example, camera image data and/or LightDetection and Ranging (LIDAR) data. For example, in someimplementations, the sensor data 140 can include a sequence of imageframes at each of a plurality of time steps. For example, the sequenceof image frames can be captured in forward-facing video on one or moreplatforms of vehicle 105.

In some implementations, the sensor data 140 can be captured via the oneor sensor(s) 125 and transmitted to the intention system 185 via network205. For example, the sensor(s) 125 can be communicatively connected tothe intention system 185. In some implementations, the sensor data 140can be captured by one or more remote computing devices (e.g., operationcomputing system 195) located remotely from the vehicle computing system100. For example, the intention system 185 can be communicativelyconnected to one or more sensors associated with another vehicle and/orthe operations computing system 195. In such a case, the intentionsystem 185 can obtain the sensor data 140, via network 205, from the oneor more remote computing devices and/or operations computing system 195.

The sensor data 140 can be associated with a surrounding environment ofthe vehicle 105. More particularly, the sensor data 140 can include oneor more objects of interest within the surrounding environment of thevehicle 105. The one or more object(s) of interest can include anymoveable object within a threshold distance from the vehicle 105. Insome implementations, the threshold distance can include a predetermineddistance (e.g., the detection range of sensor(s) 125). Additionally, oralternatively, the intention system 185 can dynamically determine thethreshold distance based on one or more factors such as weather, roadwayconditions, environment, etc. For example, the one or more factor(s) canindicate a potentially hazardous situation (e.g., heavy rain,construction, etc.). In such a case, the intention system 185 candetermine a larger threshold distance to increase safety.

In some implementations, the one or more object(s) of interest caninclude one or more vehicle(s) of interest. The vehicle(s) of interestcan include, for example, any motorized object (e.g., motorcycles,automobiles, etc.). The vehicle(s) of interest (e.g., autonomousvehicles, non-autonomous vehicles, etc.) can be equipped with specifichardware to facilitate intent-related communication. For example, theone or more vehicle(s) of interest can include one or more signallight(s) (e.g., turn signals, hazard lights, etc.) to signal thevehicle's intention. The vehicle intention, for example, can includefuture actions such as lane changes, parking, one or more turns, and/orother actions. For instance, a vehicle can signal its intention to stayin a parked position by simultaneously toggling two turn signals on/offin a blinking pattern (e.g., by turning on its hazard lights). In otherscenarios, a vehicle can signal its intention to turn by toggling asingle turn signal on/off.

The intention system 185 can include an attention model 210 configuredto identify the signals of an object. For example, attention model 210can obtain the sensor data 140. And, in some implementations, theattention model 210 can analyze the sensor data 140 to determine one ormore region(s) of interest 230. For instance, the attention model 210can process one or more image frame(s) of the sensor data 140 using oneor more machine learning techniques.

By way of example, in some implementations, the attention model 210, canprocess one or more input frames (e.g., image frames) by applying aspatial mask. For instance, the attention model 210 can resize the imageframes to a fixed 224×224 pixels. A 4-layer, fully convolutional networkcan be utilized to compute a pixel-wise, scalar attention value. Forexample, Kernels can be 3×3 with dilations (1, 2, 2, 1) and channeldimensions (32, 64, 64, 1). The resulting scalar mask can be point-wisemultiplied with the original, resized input frames (e.g., image frames).This implementation can be beneficial, for example, as it allows anetwork to add more saliency to relevant pixels and attenuate noisyspatial artifacts. In this manner, the attention model 210 can apply aspatial mask to extract the one or more region(s) of interest 230 fromthe sensor data 140.

The one or more region(s) of interest 230 can include one or morecropped image frame(s) associated with an object of interest. By way ofexample, FIG. 3 depicts an example region of interest 300 according toexample implementations of the present disclosure. The region ofinterest 300 can include an image frame (e.g., captured via one or moresensor(s) 125) associated with the surrounding environment of thevehicle 105. The region of interest 300 can include at least one objectof interest 310. For example, the region of interest 300 can include acropped image frame of an axis-aligned region of interest 300 around theobject of interest 310. Moreover, in some implementations, the region ofinterest 300 can include at least one vehicle of interest. In such acase, the region of interest 300 can include a cropped image frame of anaxis-aligned region of interest 300 around the vehicle of interest.

In addition, the region of interest 300 can include one or more signals(e.g., 320/330) associated with the object of interest 310. By way ofexample, where the region of interest 300 includes the vehicle ofinterest, the region of interest 300 can include signal lights 320and/or 330 associated with the vehicle of interest. In someimplementations, the region of interest 300 can include one or morestates associated with the one or more signal(s) 320 and/or 330. Forinstance, signal light(s) 320/330 associated with the vehicle ofinterest can be illuminated or not depending on a time associated withthe region of interest 300. Additionally, or alternatively, the regionof interest 300 can include other signal(s) such as hand movementsassociated with the object of interest 310. Moreover, the region ofinterest 300 can include one or more state(s) associated with the othersignals (e.g., different movement patterns, etc.). In this manner, theregion of interest 300 can include one cropped image frame of astreaming input of cropped image frames providing information associatedwith the object of interest 310 (e.g., vehicle of interest) over time.

Turning back to FIG. 2, the intention system 185 can include a semanticunderstanding model 215 configured to determine one or more spatialfeature(s) 235 associated with the region(s) of interest 230. Forexample, the semantic understanding model 215 can be configured toidentify occlusion and the direction from which an object is beingviewed. In some implementations, the semantic understanding model 215can provide the one or more region(s) of interest 230 as input to one ormore machine learned model(s) configured to determine the one or morespatial feature(s) 235. For instance, at least one of the machinelearned model(s) utilized by the intention system 185 (e.g., thesemantic understanding model 215) can include a convolutional neuralnetwork (e.g., a VGG16 based convolutional neural network) and/oranother type of model.

By way of example, in some implementations, a deep convolutional networkcan be used to recover spatial concept(s). Spatial feature(s) 235, forexample, can be extracted using a VGG16 based convolutional neuralnetwork architecture. In such a case, weights can be pre-trained on asoftware application, such as ImageNet, and fine-tuned during training.The machine learned model (e.g., semantic understanding model 215) canthereby extract one or more spatial feature(s) 235 associated with theregion(s) of interest 230. For example, this can allow the semanticunderstanding model 215 to model at least one of an object of interest310, the orientation of the object of interest 310, occluding objects,and/or other spatial concepts. Moreover, in some implementations, themachine learned model can produce a 7×7×512 output that can retain acoarse spatial dimension for temporal processing by a convolutionalLSTM.

As discussed above, the one or more spatial feature(s) 235 can include amodel representation of the object of interest 310. For example, themodel representation can include a model representation of the vehicleof interest. The model representation of the object of interest 310 caninclude one or more physical characteristics associated with the objectof interest 310. By way of example, where the object of interest 310 isthe vehicle of interest, the physical characteristic(s) can includeinformation associated with the vehicle of interest such as, forexample, vehicle type, position, orientation, etc. The modelrepresentation can be two-dimensional, three-dimensional, etc.

For example, in some implementations, the model representation of theobject of interest 310 can identify an object orientation associatedwith the object of interest 310. For example, the object orientation caninclude a vehicle orientation associated with the vehicle of interest.The object orientation (e.g., vehicle orientation) can be determinedrelative to another object within the surrounding environment of thevehicle 105. For example, the object orientation (e.g., vehicleorientation) can be determined relative to one or more lane boundaries,a traffic light, a sign post such as a stop sign, another vehicle withinthe surrounding environment of vehicle 105, etc. In someimplementations, the object orientation (e.g., vehicle orientation) canbe relative to the vehicle 105. For example, the object orientation(e.g., vehicle orientation) can be based on the direction from which theobject of interest 310 (e.g., vehicle of interest) is viewed from thevehicle 105. By way of example, the object orientation (e.g., vehicleorientation) can include designations such as behind, left, front,and/or right. In such an example, each designation can identify thedirection from which the object of interest 310 (e.g., vehicle ofinterest) is viewed from the vehicle 105.

Additionally, or alternatively, the one or more spatial feature(s) 235can include one or more occluding objects. The one or more occludingobjects can include any object within the region(s) of interest 230other than the object of interest 310 (e.g., vehicle of interest). Forexample, the one or more occluding objects can include one or moreobjects disrupting the view of the object of interest 310 (e.g., vehicleof interest). More particularly, the one or more occluding objects caninclude, for example, one or more objects disrupting the view of atleast one signal associated with the object of interest 310. Forexample, where the region(s) of interest 230 include the vehicle ofinterest, the occluding object(s) can include object(s) disrupting theview of at least one signal light 320 and/or 330 (e.g., one or moreheadlights, taillights, etc.) of the vehicle of interest. For instance,the occluding object(s) can be positioned between the object of interest310 and one or more sensor(s) (e.g., sensor(s) 125) onboard the vehicle105. For example, the occluding object can be positioned in such a way(e.g., within the sensor's field of view) as to at least partially blockthe sensor(s) (e.g., sensor(s) 125) from capturing sensor data 140associated with the object of interest 310 (e.g., one or more turnsignal(s) 320/330 of the vehicle of interest).

The intention system 185 can include a temporal reasoning model 220configured to determine one or more temporal feature(s) 240 associatedwith at least one of the region(s) of interest 230. For example, in someimplementations, the temporal reasoning model 220 can provide dataindicative of a sequence regions of interest 230 at multiple time stepsas input to one or more machine learned models. For instance, at leastone machine learned model (e.g., the temporal reasoning model 220) caninclude a convolutional neural network (e.g., convolutional LSTM). Themachine learned model can extract one or more temporal feature(s) 240associated with the region(s) of interest 230.

By way of example, the temporal reasoning model 220 can input per-frameinformation (e.g., region(s) of interest 230, spatial feature(s) 235,etc.) to a convolutional LSTM to distinguish the temporal patterns ofone or more signal(s) (e.g., turn signal(s), emergency flashers, etc.)from other content. For example, in some implementations, aconvolutional LSTM (ConvLSTM) model can be used to refine the spatialfeature(s) 235 associated with a sequence of region(s) of interest 230by modeling temporal feature(s) 240 of a streaming input of region(s) ofinterest 230 (e.g., stream of feature tensors). For example, theConvolutional LSTM can learn temporal feature(s) 240 by maintaining aninternal, hidden state, which can be modified through a series ofcontrol gates.

The equation below illustrates an example algorithm for determiningtemporal feature(s) 240. For example, let X_(t) be a feature tensor(e.g., associated with region(s) of interest 230) that is input at timet, and W and B be the learned weights and biases of the ConvLSTM. Thehidden state can be embodied by two tensors, H and C, which are updatedover time by the following expressions:I _(t)=σ(W ^(xi) *{tilde over (X)} _(t) +W ^(hi) *H _(t-1) +W ^(ci) *C_(t-1) +{tilde over (B)} ^(i))  (1)F _(t)=σ(W ^(xf) *X _(t) +W ^(hf) *H _(t-1) +W ^(cf) *C _(t-1) +B^(f))  (2)C _(t) =F _(t) ·C _(t-1) +I _(t)·tan h(W ^(xc) *X _(t) +W ^(hc) *H_(t-1) +B ^(c))  (3)O _(t)=σ(W ^(xo) *X _(t) +W ^(ho) *H _(t-1) +W ^(co) *C _(t) +B^(o))  (4)H _(t) =O _(t)·tan h(C _(t)).  (5)

-   -   The parameterized gates I (input), F (forget) and O (output) can        control the flow of information through the network and how much        of it should be propagated in time. Temporal feature(s) 240 can        be maintained through cell memory, which can accumulate relevant        latent representations. For example, Equation 3 can prevent        overfitting by applying dropout on the output as a regularizer.        At Equation 1, the input gate can control the use of new        information from the input. At Equation 2, the forget gate can        control what information is discarded from the previous a cell        state. And, at Equation 3, the output gate can further control        the propagation of information from a current cell state to the        output, for instance, by element-wise multiplication at Equation        5.

In some implementations, the ConvLSTM module can be constructed as aseries of ConvLSTM layers, each following Equations (1)-(5). Forexample, in a multi-layer architecture, each subsequent layer can takeas input the hidden state, H_(t), from the preceding layer (the firstlayer takes X_(t) as input). By way of example, in some implementations,two ConvLSTM layers, each with a 7×7×256 hidden state can be utilized.Additionally, or alternatively, a variety of ConvLSTM layers can beutilized to determine temporal feature(s) 240 associated with a seriesof region(s) of interest 230.

The temporal feature(s) 240 can include temporal characteristics of theregion(s) of interest 230 (e.g., a streaming input of image data). Forexample, the temporal feature(s) 240 can include one or more semanticstate(s) associated with a signal of the object of interest 310. Forexample, the temporal feature(s) 240 can include one or more semanticstate(s) associated with at least one signal light (e.g., 320 and/or330) of the vehicle of interest over time. For instance, the one or moresemantic states can include designations such as “off,” “on,” and/or“unknown.” By way of example, “off” can indicate that a signal is notactive in a given series of region(s) of interest 230. For instance,“off” can indicate that a signal light (e.g., 320) associated with avehicle of interest (e.g., 310) is not illuminated over a time period.The designation “on” can indicate an active signal over a period oftime. For instance, “on” can indicate that a signal light (e.g., 330)associated with a vehicle of interest (e.g., 310) illuminated in somemanner over a time period. The designation “unknown” can indicate thepresence of an occluding object over a time period. For instance,“unknown” can indicate that an occluding object disrupted the view of asignal light (e.g., 330) over a time period. In this manner, thetemporal feature(s) 240 can distinguish flashing lights and persistentlights from other specious light patterns.

The intention system 185 can include a classification model 225configured to classify the resulting spatial and temporal feature(s)235/240. For example, in some implementations, the classification model225 can provide one or more temporal feature(s) 240 and one or morespatial feature(s) 235 to one or more machine learned model(s)configured to determine object intention 245. The machine learnedmodel(s) can include the same or different machine learned model(s) thatare used to determine the spatial feature(s) 235 and/or temporalfeature(s) 240. By way of example, in some implementations, at least oneof the machine learned model(s) can include a neural network. Forinstance, the feature(s) 235/240 can be passed through a fully connectedneural network to produce one or more variables of interest such asy_(t)(intent) over semantic states such as “left turn,” “right turn,”“flashers,” “off,” and “unknown;” y_(t)(left) and y_(t)(right) over thestates “on,” “off,” “unknown,” (e.g., for individual lights on the leftand right sides of the vehicle, respectively); y_(t)(view) over thestates “behind,” “left,” “right,” and “front.” In some implementations,the parameters on each of these layers can be regularized with weightdecay to prevent overfitting.

The classification model 225 can be configured to determine objectintention 245 associated with the object of interest 310. For instance,in some implementations, the classification model 225 can determine avehicle intention associated with the vehicle of interest. For example,the vehicle intention can indicate a predicted movement of the vehicleof interest such as a future left turn, right turn, emergency stop(e.g., flashers), and/or unknown.

The object intention 245 (e.g., vehicle intention) can be based, atleast in part, on the spatial feature(s) 235 and/or temporal feature(s)240. For example, the object intention 245 (e.g., vehicle intention) canbe determined based, at least in part, on the one or more semanticstate(s) associated with at least one signal of the object of interest310. For instance, a vehicle intention can be determined based, at leastin part, on the semantic state(s) associated with at least one signallight (e.g., 320/330) of the vehicle of interest. For instance, asemantic state of “on” associated with a right turn signal (e.g., 330)and a semantic state of “off” associated with a left turn signal (e.g.,320) can indicate a right turn.

Moreover, in some implementations, the object intention 245 can bedetermined based, at least in part, on the orientation of the object ofinterest 310 (e.g., vehicle of interest). For instance, in the examplescenario above regarding the right turn, the intention system 185 (e.g.,via the classification model 225) can instead determine a left turndepending on the orientation of the vehicle of interest. By way ofexample, the classification model 225 can determine a vehicle intentionindicative of a right turn when the vehicle of interest is being viewedby the vehicle 105 from behind (e.g., vehicle orientation is indicativeof “behind”). Otherwise, for example if the vehicle of interest is beingviewed from the front (e.g., vehicle orientation is indicative of“front”), the classification model 225 can determine a vehicle intentionindicative of a left turn (e.g., the turn signal on the right side ofthe vehicle of interest identifies a left turn rather than a rightturn). Thus, by accounting for the orientation of the object of interest310, the intention system 185 can improve the accuracy of objectintentions 245 for a diverse set of real-world scenarios.

The intention system 185 can initiate one or more actions based, atleast in part, on the object intention 245. The one or more actions caninclude, for example, planning safe maneuvers, issuing one or moreinformational prompts, etc. For example, the intention system 185 cancommunicate, via network 205, with the autonomy system 130 of anautonomous vehicle (e.g., the vehicle 105). For instance, the motionplanning system 165 can generate a motion plan 180 based, at least inpart, on the object intention 245.

By way of example, a bus (e.g., the vehicle of interest) can signal itsintention to make a stop to pick up and drop off passengers by turningon one or more signal light(s) 320 and/or 330 (e.g., emergencyflashers). In such a case, the intention system 185 can initiate one ormore actions based on the vehicle intention to stop as indicated by theemergency flashers. For example, in the event that the vehicle 105 is anautonomous vehicle, the intention system 185 can provide data indicativeof the vehicle intention to stop to the vehicle's autonomy system 130(or sub-systems) such that the vehicle 105 can generate one or moremotion plan(s) 180 to avoid the stopped bus (e.g., changing lanes,decelerating, etc.). Additionally, in some implementations, theintention system 185 can initiate the identified motion plan 180 (e.g.,to safely avoid any interference with the bus).

As another example, a truck (and/or the operator thereof) may intend tochange lanes such that the truck will be in front of the vehicle 105.Beforehand, the truck/truck operator can signal its intention byactivating one or more of the truck's signal lights 320 and/or 330(e.g., a right turn signal 320). In such a case, the intention system185 can determine a vehicle intention to change lanes in front of thevehicle 105. The intention system 185 can communicate with the autonomysystem 130 (or sub-system) such that the vehicle 105 can generate one ormore motion plan(s) 180 to avoid the truck (e.g., by decreasing itsspeed, changing lane, etc.). In response, the intention system 185 caninitiate one or more the motion plan(s) 180. For example, the intentionsystem 185 can initiate one or more actions such as decelerating,changing a lane, etc.

Additionally, or alternatively, the intention system 185 can initiate acommunication with one or more vehicle operator(s) 106. For example, theintention system 185 can communicate (e.g., via network 205) with one ormore output device(s) (e.g., one or more output device(s) of the vehicle105, an output device of a user device associated with the vehicleoperator 106, HMI 190, etc.) to initiate one or more informationalprompts. For example, the intention system 185 can initiate a prompt,via one or more output device(s) of vehicle 105, to the vehicle operator106. For instance, the vehicle 105 can issue a warning associated with abus's intention to stop and/or present a recommended maneuver to thevehicle operator 106. By way of example, the intention system 185 caninitiate a warning of a sudden stop and suggest a maneuver to changelanes. In this manner, the intention system 185 can reduce delays andcongestions on the roadways, while also increasing the safety ofobject(s) of interest and the vehicles, by providing relevantinformation to vehicle operators (e.g., such as vehicle operator 106)and accounting for future actions of objects within the surroundingenvironment of the vehicle 105 when determining motion plan(s) 180.

Turning to FIG. 4, FIG. 4 depicts an example model architecture 400according to example implementations of the present disclosure. Theintention system 185 can utilize a variety of machine learned modelconfigurations, for example, to determine the one or more temporalfeature(s) 240 and the one or more spatial feature(s) 235. For example,in some implementations, the same machine learned model can be trainedto determine the temporal feature(s) 240 and the spatial feature(s) 235.Additionally, or alternatively, the temporal feature(s) 240 can bedetermined separately from the spatial feature(s) 235. For instance, thetemporal feature(s) 240 can be determined via a first machine learnedmodel (e.g., a convolutional LSTM), while the spatial features 235 canbe determined by a second machine learned model (e.g., a convolutionalneural network). By way of example, the spatial and temporal feature(s)235/240 can be factored into separate modules. Factorization, forexample, can be utilized to more efficiently use available computingresources and increase performance.

Moreover, the temporal feature(s) 240 and the spatial feature(s) 235 canbe determined sequentially or in parallel. For instance, the intentionsystem 185 can input the one or more region(s) of interest 230 into twomachine learned model(s) to determine the one or more temporalfeature(s) 240 and the one or more spatial feature(s) 235 in parallel.In some implementations, the intention system 185 can sequentiallydetermine the spatial feature(s) 235 and the temporal feature(s) 240 ina predetermined order. For example, the intention system 185 can firstinput the one or more region(s) 230 of interest into a machine learnedmodel to determine the spatial feature(s) 235 and subsequently input theregion(s) of interest 230 and the spatial feature(s) 235 into the sameor a different machine learned model to determine the temporalfeature(s) 240. In some implementations, the intention system 185 canfirst determine the temporal feature(s) 240 and subsequently determinethe spatial feature(s) 235.

In some implementations, the intention system 185 can utilize aconvolutional-recurrent architecture to classify an object intention 245associated with the object of interest 310. For instance, the intentionsystem 185 can utilize the convolutional-recurrent architecture toclassify the state of turn signal(s) such as turn signals 320 and/or 330associated with the vehicle of interest. In some implementations, theattention model 210 can predict an attention mask for each originalinput frame (e.g., region of interest 300) using a convolutional network(e.g., fully convolutional network). In addition, or alternatively, thespatial understanding model 215 can take the element-wise product withthe original input image (e.g., region of interest 300) and extractspatial feature(s) 235 using a convolutional neural network (e.g., aVGG16-based convolutional neural network). The temporal reasoning model220 can then incorporate one or more temporal feature(s) 240 using aconvolutional network (e.g., a convolutional LSTM). In this manner,probability distributions associated with an object intention 245 can bepredicted based on the spatial and temporal feature(s) 235/240. Forexample, the probability distributions can be predicted over temporalfeature(s) 240 such as the state of turn signal(s) (e.g., 320/330)and/or spatial feature(s) 235 such as the view face (e.g., objectorientation) of the object of interest 310.

Although the above description provides examples that discuss vehiclesof interest 310, the intention system 185 is not limited to vehicles andcan be applied to any object of interest 310 within vehicle's 105surrounding environment. For example, in some implementations, theintention system 185 can be configured to determine the intention of oneor more bicycle(s) within the surrounding environment of vehicle 105.For example, the attention model 210 can be configured to determine oneor more region(s) of interest 230 including one or more bicycle(s) ofinterest 310 (e.g., using machine learned model(s) that have beentrained to analyze signals associated with a bicycle). In someimplementations, the region(s) of interest 230 can include one or moresignal(s) (e.g., hand waves by an operator of a bicycle) associated withthe bicycle(s) of interest 310. Moreover, the semantic understandingmodel 215 can determine one or more spatial feature(s) 235 associatedwith the region(s) of interest 230. For example, the spatial feature(s)235 can include a bicycle orientation. In addition, the temporalreasoning model 220 can determine one or more temporal feature(s) 240associated with the region(s) of interest 230. And, the classificationmodel 225 can determine, based, at least in part, on the spatialfeature(s) and temporal feature(s) 235/240, a bicycle intentionassociated with the bicycle of interest 310. Moreover, the intentionmodel 185 can initiate one or more action(s) based on the bicycleintention. For example, an action can include outputting data forautonomous vehicle operation, providing data for display to an operatorvia the HMI 190, etc.

FIG. 5 depicts an example flow diagram of an example method 500 fordetermining semantic object intentions according to exampleimplementations of the present disclosure. One or more portion(s) of themethod 500 can be can be implemented by a computing system that includesone or more computing devices such as, for example, the computingsystems described with reference to the other figures (e.g., the vehiclecomputing system 100, the intention system 185, the operations computingsystem 195, etc.). Each respective portion of the method 500 can beperformed by any (or any combination) of one or more computing devices.Moreover, one or more portion(s) of the method 500 can be implemented asan algorithm on the hardware components of the device(s) describedherein (e.g., as in FIGS. 1-2, 4, 6, and/or 7), for example, todetermine an object intention 245 based on physical attributes. FIG. 5depicts elements performed in a particular order for purposes ofillustration and discussion. Those of ordinary skill in the art, usingthe disclosures provided herein, will understand that the elements ofany of the methods discussed herein can be adapted, rearranged,expanded, omitted, combined, and/or modified in various ways withoutdeviating from the scope of the present disclosure. FIG. 5 is describedwith reference to elements/terms described with respect to other systemsand figures for example illustrated purposes and is not meant to belimiting. One or more portions of method 500 can be performedadditionally, or alternatively, by other systems.

At (505), the method 500 can include obtaining sensor data 140. Forexample, the intention system 185 can obtain sensor data 140 associatedwith a surrounding environment of a first vehicle (e.g., vehicle 105).For instance, an autonomous vehicle (e.g., vehicle 105) can obtain, viaone or more vehicle sensors 125, sensor data 140 associated with asurrounding environment of the autonomous vehicle (e.g., vehicle 105).In some implementations, the sensor data 140 can include a sequence ofimage frames, each image frame corresponding to one of a plurality oftime steps.

At (510), the method 500 can include determining region(s) of interest230. For example, the intention system 185 can determine one or moreregion(s) of interest 230 associated with the sensor data 140. The oneor more region(s) of interest 230 can include one or more cropped imageframes associated with the object of interest 310. For instance, the oneor more region(s) of interest 230 can include one or more cropped imageframes associated with the vehicle of interest. In such an example, theone or more cropped image frame(s) can also include data indicative ofone or more signal light(s) 320 and/or 330 associated with the vehicleof interest. By way of example, the intention system 185 can determinethe one or more region(s) of interest 230 via one or more machinelearning techniques. For example, determining the one or more region(s)of interest 230 can include inputting one or more image frames into amachine learned model.

At (515), the method 500 can include determining spatial feature(s) 235.For example, the intention system 185 can determine one or more spatialfeature(s) 235 associated with at least one of the one or more region(s)of interest 230. In some implementations, the intention system 185 candetermine the one or more spatial feature(s) 235 associated with atleast one of the one or more region(s) of interest 230 via one or moremachine learning models. For example, determining the one or morespatial feature(s) 235 can include inputting the one or more region(s)of interest 230 into at least one machine learned model.

In some implementations, at least one of the one or more spatialfeature(s) 235 can be indicative of an object orientation associatedwith the object of interest 310. For example, at least one of the one ormore spatial feature(s) 235 can be indicative of a vehicle orientationassociated with the vehicle of interest. The vehicle orientation, forexample, can be relative to a first vehicle (e.g., vehicle 105).Moreover, in some implementations, the one or more spatial feature(s)235 can be indicative of a model representation of the vehicle ofinterest. The model representation of the vehicle of interest, forexample, can be indicative of the vehicle orientation associated withthe vehicle of interest.

In some implementations, at least one of the one or more spatialfeature(s) 235 can be indicative of one or more occluding objects. Theone or more occluding objects, for example, can include one or moreobject(s) disrupting the view of the object of interest 310. Forexample, the one or more occluding objects can include one or moreobject(s) disrupting the view of the vehicle of interest from a firstvehicle (e.g., vehicle 105).

At (520), the method 500 can include determining temporal feature(s)240. For example, the intention system 185 can determine one or moretemporal feature(s) 240 associated with at least one of the one or moreregion(s) of interest 230. In some implementations, the intention system185 can determine the one or more temporal feature(s) 240 associatedwith at least one of the one or more region(s) of interest 230 via oneor more machine learning models. For example, determining the one ormore temporal feature(s) 140 can include inputting a series of regionsof interest 230 into at least one machine learned model.

In some implementations, the one or more temporal feature(s) 240 can beindicative of one or more semantic states associated with at least onesignal of the object of interest 310. For example, the one or moretemporal feature(s) 140 can be indicative of one or more semantic statesassociated with at least one signal light (e.g., signal light(s) 320and/or 330 of the vehicle of interest). For instance, the semanticstate(s) can include an indication of whether a signal light is “on”and/or “off” over a period of time and/or whether the signal light isoccluded over a period of time (e.g., “unknown”).

At (525), the method 500 can include determining intention associatedwith an object. For example, the intention system 185 can determine anobject intention 245 associated with the object of interest 310.Moreover, the intention system 185 can determine an intention associatedwith the vehicle of interest. In some implementations, the intentionsystem 185 can determine the intention associated with the object and/orvehicle of interest via one or more machine learning models. The objectand/or vehicle intention can be based, at least in part, on the one ormore spatial feature(s) 235 and the one or more temporal feature(s) 240.For example, determining the object and/or vehicle intention can includeinputting the one or more spatial feature(s) 235 and the one or moretemporal feature(s) 240 into at least one machine learned model. In someimplementations, the region(s) of interest 230, spatial feature(s) 235,temporal feature(s) 240, and object and/or vehicle intention can bedetermined separately via one or more different machine learned models.

At (530), the method 500 can include initiating one or more action(s).For example, the intention system 185 can initiate one or more action(s)based, at least in part, on the intention. Moreover, in someimplementations, the intention system 185 can initiate one or moreaction(s) based, at least in part, on the intention.

For instance, the one or more action(s) can include providing one ormore informational prompt(s) to an operator of the first vehicle (e.g.,vehicle 105). For example, an autonomous vehicle (e.g., vehicle 105) caninclude one or more output device(s) (e.g., HMI 190). The autonomousvehicle (e.g., vehicle 105) can provide, via the one or more outputdevice(s), data indicative of the intention associated with the vehicleof interest to one or more operator(s) (e.g., operator 106) of theautonomous vehicle (e.g., vehicle 105). Moreover, the one or moreaction(s) can include generating a motion plan 180 for the autonomousvehicle (e.g., vehicle 105) based, at least in part, on the intentionassociated with a vehicle of interest. In addition, or alternatively,the one or more action(s) can further include initiating the one or moreaction(s) based, at least in part, on the motion plan 180.

Various means can be configured to perform the methods and processesdescribed herein. For example, FIG. 6 depicts an example system 600 thatincludes various means according to example embodiments of the presentdisclosure. The computing system 600 can be and/or otherwise include,for example, the intention system 185. The computing system 600 caninclude data obtaining unit(s) 605, region of interest unit(s) 610,spatial feature unit(s) 615, temporal feature unit(s) 620, objectintention unit(s) 625, operator communication unit(s) 630, motioncontrol unit(s) 635, storing unit(s) 640 and/or other means forperforming the operations and functions described herein. In someimplementations, one or more of the units may be implemented separately.In some implementations, one or more units may be a part of or includedin one or more other units. These means can include processor(s),microprocessor(s), graphics processing unit(s), logic circuit(s),dedicated circuit(s), application-specific integrated circuit(s),programmable array logic, field-programmable gate array(s),controller(s), microcontroller(s), and/or other suitable hardware. Themeans can also, or alternately, include software control meansimplemented with a processor or logic circuitry for example. The meanscan include or otherwise be able to access memory such as, for example,one or more non-transitory computer-readable storage media, such asrandom-access memory, read-only memory, electrically erasableprogrammable read-only memory, erasable programmable read-only memory,flash/other memory device(s), data registrar(s), database(s), and/orother suitable hardware.

The means can be programmed to perform one or more algorithm(s) forcarrying out the operations and functions described herein. Forinstance, the means (e.g., the data obtaining unit(s)) can be configuredto obtain sensor data associated with a surrounding environment of afirst vehicle (e.g., from one or more sensors onboard the firstvehicle). As described herein, the sensor data can be indicative of avariety of information such as, for example, a sequence of image framesat each of a plurality of time steps.

The means (e.g., the region of interest unit(s) 610) can determine oneor more region(s) of interest 230 associated with the sensor data 140.For example, the means (e.g., the region of interest unit(s) 610) caninclude an attention model 210 configured to analyze the sensor data 140to determine one or more region(s) of interest 230. For instance, insome implementations, the means (e.g., the region of interest unit(s)610) can utilize one or more machine learned models (e.g., attentionmodel 210) to determine the one or more region(s) of interest 230. Asdescribed herein, the one or more region(s) of interest 230 can includeone or more cropped image frames associated with the object of interest310. For example, the one or more cropped image frames can include dataindicative of one or more signal light(s) associated with the object ofinterest 310.

The means (e.g., the spatial feature unit(s) 615) can determine one ormore spatial feature(s) 235 associated with at least one of the one ormore region(s) of interest 230. For example, the means (e.g., thespatial feature unit(s) 615) can include a semantic understanding model215 configured to determine one or more spatial feature(s) 235associated with the region(s) of interest 230. For instance, in someimplementations, the means (e.g., the spatial feature unit(s) 615) canutilize one or more machine learned models (e.g., semantic understandingmodel 215) to determine the one or more spatial feature(s) 235. Asdescribed herein, the spatial feature(s) 235 can include one or moreobject characteristics. For example, the spatial feature(s) 235 canindicate an orientation of the object of interest 310. For instance, thespatial feature(s) 235 can indicate a vehicle orientation associatedwith the vehicle of interest. The means (e.g., the temporal featureunit(s) 620) can determine one or more temporal feature(s) 240associated with at least one of the one or more region(s) of interest230. For example, the means (e.g., the temporal feature unit(s) 620) caninclude a temporal reasoning model 220 configured to determine one ormore temporal feature(s) 240 associated with at least one of theregion(s) of interest 230. For instance, in some implementations, themeans (e.g., the temporal feature unit(s) 620) can utilize one or moremachine learned models (e.g., temporal reasoning model 220) to determinethe one or more temporal feature(s) 240. As described herein, thetemporal feature(s) 240 can be indicative of one or more semanticstate(s) associated with the object of interest 310. For example, thesemantic state(s) can be associated with at least one signal light(e.g., 320/330) of the vehicle of interest.

The means (e.g., the object intention unit(s) 625) can determine anobject intention 245 associated with the object of interest 310 based,at least in part, on the one or more spatial feature(s) 235 and the oneor more temporal feature(s) 240. For example, the means (e.g., theobject intention unit(s) 625) can include a classification model 225configured to determine an object intention 245. For instance, in someimplementations, the means (e.g., the object intention unit(s) 625) canutilize one or more machine learned models (e.g., classification model225) to determine an object intention 245. As described herein, theobject intention 245 can include one or more future acts by the objectof interest 310 (e.g., as intended). For instance, the object intention245 can include a future left turn, right turn, and/or stop associatedwith the object of interest 310.

The means (e.g., operator communication unit(s) 630 and/or the motioncontrol unit(s) 635) can initiate one or more actions based, at least inpart, on the object intention 245. For example, the (e.g., operatorcommunication unit(s) 630) can provide data indicative of the objectintention 245 to one or more operators 106 (e.g., via at least one of aoutput device of the vehicle 105, an output device of a user deviceassociated with the operator 106, HMI 190, etc.) of the vehicle 105.Moreover, the means (e.g., motion control unit(s) 635) can determine oneor more motion plan(s) 180 based, at least in part, on the objectintention 245. In addition, or alternatively, the means (e.g., motioncontrol unit(s) 635) can initiate one or more action(s) based, at leastin part, on the motion plan(s) 180. The means (e.g., storing unit(s)640) can be configured for storing data. For instance, the means (e.g.,the storing unit(s) 640) can be configured for storing data indicativeof user input, object data, sensor data (e.g., sequence of imageframes), region(s) of interest 230, spatial feature(s) 235, temporalfeature(s) 240, object intention(s) 245, training data utilized to trainone or more machine learned model(s), etc. in a memory.

These described functions of the means are provided as examples and arenot meant to be limiting. The means can be configured for performing anyof the operations and functions described herein.

FIG. 7 depicts example system components of an example system 700according to example implementations of the present disclosure. Theexample system 700 illustrated in FIG. 7 is provided as an example only.The components, systems, connections, and/or other aspects illustratedin FIG. 7 are optional and are provided as examples of what is possible,but not required, to implement the present disclosure. The examplesystem 700 can include an intention system 185 and a machine learningcomputing system 750 that are communicatively coupled over one or morenetwork(s) 745. As described herein, the intention system 185 can beimplemented onboard a vehicle (e.g., as a portion of the vehiclecomputing system 100) and/or can be remote from a vehicle (e.g., as aportion of an operations computing system 195). In either case, avehicle computing system 100 can utilize the operations and model(s) ofthe intention system 185 (e.g., locally, via wireless networkcommunication, etc.).

The intention system 185 can include one or computing device(s) 710. Thecomputing device(s) 710 of the intention system 185 can includeprocessor(s) 715 and a memory 720. The one or more processor(s) 715 canbe any suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 720 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and/or combinations thereof.

The memory 720 can store information that can be obtained by the one ormore processor(s) 715. For instance, the memory 720 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices, etc.)can include computer-readable instructions 725 that can be executed bythe one or more processors 715. The instructions 725 can be softwarewritten in any suitable programming language or can be implemented inhardware. Additionally, or alternatively, the instructions 725 can beexecuted in logically and/or virtually separate threads on processor(s)715.

For example, the memory 720 can store instructions 725 that whenexecuted by the one or more processors 715 cause the one or moreprocessors 715 (e.g., of the intention system 185) to perform operationssuch as any of the operations and functions of the intention system 185and/or for which the intention system 185 is configured, as describedherein, the operations for determining object intent based on physicalattributes (e.g., one or more portions of method 500), the operationsand functions of any of the models described herein and/or for which themodels are configured and/or any other operations and functions for theintention system 185, as described herein.

The memory 720 can store data 730 that can be obtained (e.g., received,accessed, written, manipulated, generated, created, stored, etc.). Thedata 730 can include, for instance, sensor data, input data, dataindicative of machine-learned model(s), output data, sparse geographicdata, and/or other data/information described herein. In someimplementations, the computing device(s) 710 can obtain data from one ormore memories that are remote from the intention system 185.

The computing device(s) 710 can also include a communication interface735 used to communicate with one or more other system(s) (e.g., othersystems onboard and/or remote from a vehicle, the other systems of FIG.7, etc.). The communication interface 735 can include any circuits,components, software, etc. for communicating via one or more networks(e.g., 745). In some implementations, the communication interface 735can include, for example, one or more of a communications controller,receiver, transceiver, transmitter, port, conductors, software and/orhardware for communicating data/information.

According to an aspect of the present disclosure, the intention system185 can store or include one or more machine-learned models 740. Asexamples, the machine-learned model(s) 740 can be or can otherwiseinclude various machine-learned model(s) such as, for example, neuralnetworks (e.g., deep neural networks), support vector machines, decisiontrees, ensemble models, k-nearest neighbors models, Bayesian networks,or other types of models including linear models and/or non-linearmodels. Example neural networks include feed-forward neural networks(e.g., convolutional neural networks, etc.), recurrent neural networks(e.g., long short-term memory recurrent neural networks, etc.), and/orother forms of neural networks. The machine-learned models 740 caninclude the machine-learned models described herein with reference toFIGS. 2, and 4-6.

In some implementations, the intention system 185 can receive the one ormore machine-learned models 740 from the machine learning computingsystem 750 over the network(s) 745 and can store the one or moremachine-learned models 740 in the memory 720 of the intention system185. The intention system 185 can use or otherwise implement the one ormore machine-learned models 740 (e.g., by processor(s) 715). Inparticular, the intention system 185 can implement the machine learnedmodel(s) 740 to determine object intent based on physical attributes, asdescribed herein.

The machine learning computing system 750 can include one or moreprocessors 755 and a memory 765. The one or more processors 755 can beany suitable processing device (e.g., a processor core, amicroprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.)and can be one processor or a plurality of processors that areoperatively connected. The memory 765 can include one or morenon-transitory computer-readable storage media, such as RAM, ROM,EEPROM, EPROM, one or more memory devices, flash memory devices, etc.,and/or combinations thereof.

The memory 765 can store information that can be accessed by the one ormore processors 755. For instance, the memory 765 (e.g., one or morenon-transitory computer-readable storage mediums, memory devices, etc.)can store data 775 that can be obtained (e.g., generated, retrieved,received, accessed, written, manipulated, created, stored, etc.). Insome implementations, the machine learning computing system 750 canobtain data from one or more memories that are remote from the machinelearning computing system 750.

The memory 765 can also store computer-readable instructions 770 thatcan be executed by the one or more processors 755. The instructions 770can be software written in any suitable programming language or can beimplemented in hardware. Additionally, or alternatively, theinstructions 770 can be executed in logically and/or virtually separatethreads on processor(s) 755. The memory 765 can store the instructions770 that when executed by the one or more processors 755 cause the oneor more processors 755 to perform operations. The machine learningcomputing system 750 can include a communication interface 760,including devices and/or functions similar to that described withrespect to the intention system 185.

In some implementations, the machine learning computing system 750 caninclude one or more server computing devices. If the machine learningcomputing system 750 includes multiple server computing devices, suchserver computing devices can operate according to various computingarchitectures, including, for example, sequential computingarchitectures, parallel computing architectures, or some combinationthereof.

In addition, or alternatively to the model(s) 740 at the intentionsystem 185, the machine learning computing system 750 can include one ormore machine-learned model(s) 780. As examples, the machine-learnedmodel(s) 780 can be or can otherwise include various machine-learnedmodels such as, for example, neural networks (e.g., deep neuralnetworks), support vector machines, decision trees, ensemble models,k-nearest neighbors models, Bayesian networks, or other types of modelsincluding linear models and/or non-linear models. Example neuralnetworks include feed-forward neural networks (e.g., convolutionalneural networks), recurrent neural networks (e.g., long short-termmemory recurrent neural networks, etc.), and/or other forms of neuralnetworks. The machine-learned models 780 can be similar to and/or thesame as the machine-learned models 740, and/or any of the modelsdiscussed herein with reference to FIGS. 2, 4-6.

As an example, the machine learning computing system 750 can communicatewith the intention system 185 according to a client-server relationship.For example, the machine learning computing system 750 can implement themachine-learned models 780 to provide a web service to the intentionsystem 185 (e.g., including on a vehicle, implemented as a system remotefrom the vehicle, etc.). For example, the web service can providemachine-learned models to an entity associated with a vehicle; such thatthe entity can implement the machine-learned model (e.g., to determineobject intent, etc.). Thus, machine-learned models 780 can be locatedand used at the intention system 185 (e.g., on the vehicle 105, at theoperations computing system 195, etc.) and/or the machine-learned models780 can be located and used at the machine learning computing system750.

In some implementations, the machine learning computing system 750and/or the intention system 185 can train the machine-learned model(s)740 and/or 780 through the use of a model trainer 785. The model trainer785 can train the machine-learned models 740 and/or 780 using one ormore training or learning algorithm(s). One example training techniqueis backwards propagation of errors. In some implementations, the modeltrainer 785 can perform supervised training techniques using a set oflabeled training data. In other implementations, the model trainer 785can perform unsupervised training techniques using a set of unlabeledtraining data. The model trainer 785 can perform a number ofgeneralization techniques to improve the generalization capability ofthe models being trained. Generalization techniques include weightdecays, dropouts, or other techniques.

In some implementations, the model trainer 785 can utilize lossfunction(s) to train the machine-learned model(s) 740 and/or 780. Forexample, multi-task loss can be used to teach a model(s) (e.g., 740and/or 780) utilized to detect region(s) of interest 230, spatialfeature(s) 235, temporal feature(s) 240, and/or object intention(s) 245.By way of example, a weighted cross entropy loss over defined tasks canbe employed. For example, in some implementations, given model inputs x,ground truth labels ŷ, model weights θ, task weights γ and networkfunction σ(⋅), the loss can be defined as:

(ŷ,x|θ)=l _(intent)(ŷ,x|θ)+l _(left)(ŷ,x|θ)+l _(right)(ŷ,x|θ)+l_(view)(ŷ,x|θ)where each task loss can use cross-entropy and is defined as:

${\ell\left( {\hat{y},\left. x \middle| \theta \right.} \right)} = {\lambda{\sum\limits_{c}^{\;}{{\hat{y}}_{c}{\log\left( {\sigma_{c}\left( x \middle| \theta \right)} \right)}}}}$For example, the loss can be defined in terms of a sum over the taskspace, which can include: l(intent), (e.g., the loss over the high levelunderstanding of the actor); l(left) and l(right), (e.g., the lossesover the left and right turn signals, respectively); and l(view), (e.g.,the loss over the face of the actor that is seen).

The model trainer 780 can train a machine-learned model (e.g., 740and/or 780) based on a set of training data 790. The training data 790can include, for example, labeled datasets (e.g., turn signalclassification datasets, etc.). By way of example, 1,257,591 labeledframes (e.g., image frames) including over 10,000 vehicle trajectoriesrecorded over an autonomous driving platform at 10 Hz in terms of thestate of turn signals can be used. In such an example, each frame can belabeled for a left turn and right turn light in terms of “on,” “off,” or“unknown.” In some implementations, the label(s) can identify theconceptual state of each light, with “on” indicating that the signal isactive even when the light bulb is not illuminated. From these labels, ahigh-level action such as object intent can be inferred.

The training data 790 can be taken from the same vehicle as that whichutilizes the model(s) 740 and/or 780. Accordingly, the model(s) 740and/or 780 can be trained to determine outputs in a manner that istailored to that particular vehicle. Additionally, or alternatively, thetraining data 790 can be taken from one or more different vehicles thanthat which is utilizing the model(s) 740 and/or 780. The model trainer785 can be implemented in hardware, firmware, and/or softwarecontrolling one or more processors. Additionally, or alternatively,other data sets can be used to train the model(s) (e.g., models 740and/or 780) including, for example, publicly accessible datasets (e.g.,labeled data sets, unlabeled data sets, etc.).

To train the model(s) (e.g., models 740 and/or 780), Adam optimizationwith a learning rate of 1×10⁻⁴, β₁=0.9, and β₂=0.999 can be utilized.Moreover, the learning rate on plateau can be reduced, multiplying it bya factor of 0.1 if 5 epochs go by without changing the loss by more than1×10⁻³. A weight decay of 1×10⁻⁴ and dropout with p=0.5 can be used infully connected layers (e.g., those used to classify object intention245) for regularization. In some implementations, training mini-batchescan be sampled using a stratified scheme that counteracts classimbalance. For example, training can be limited to 50 epochs andselection can be done according to validation metrics. Additionally, oralternatively, data augmentation can be utilized, for example, randommirroring and color jittering can be applied to input sequences (e.g.,sequence of image frames).

In this way, the model(s) 740 and/or 780 can be designed to determineobject intention 245 by learning to determine correlating spatial andtemporal feature(s) 235/240 from sensor data 140. For example, themodel(s) 740 and/or 780 can learn to determine an object intention 245based, at least in part, on determined spatial and temporal feature(s)235/240 associated with sensor data 140 including one or more imageframes.

The network(s) 745 can be any type of network or combination of networksthat allows for communication between devices. In some embodiments, thenetwork(s) 745 can include one or more of a local area network, widearea network, the Internet, secure network, cellular network, meshnetwork, peer-to-peer communication link and/or some combination thereofand can include any number of wired or wireless links. Communicationover the network(s) 745 can be accomplished, for instance, via a networkinterface using any type of protocol, protection scheme, encoding,format, packaging, etc.

FIG. 7 illustrates one example system 700 that can be used to implementthe present disclosure. Other computing systems can be used as well. Forexample, in some implementations, the intention system 185 can includethe model trainer 785 and the training dataset 790. In suchimplementations, the machine-learned models 740 can be both trained andused locally at the intention system 185 (e.g., at the vehicle 105).

Computing tasks discussed herein as being performed at computingdevice(s) remote from the vehicle 105 can instead be performed at thevehicle 105 (e.g., via the vehicle computing system 100), or vice versa.Such configurations can be implemented without deviating from the scopeof the present disclosure. The use of computer-based systems allows fora great variety of possible configurations, combinations, and divisionsof tasks and functionality between and among components.Computer-implemented operations can be performed on a single componentor across multiple components. Computer-implemented tasks and/oroperations can be performed sequentially or in parallel. Data andinstructions can be stored in a single memory device or across multiplememory devices.

While the present subject matter has been described in detail withrespect to specific example embodiments and methods thereof, it will beappreciated that those skilled in the art, upon attaining anunderstanding of the foregoing can readily produce alterations to,variations of, and equivalents to such embodiments. Accordingly, thescope of the present disclosure is by way of example rather than by wayof limitation, and the subject disclosure does not preclude inclusion ofsuch modifications, variations and/or additions to the present subjectmatter as would be readily apparent to one of ordinary skill in the art.

What is claimed is:
 1. A computer-implemented method of determiningsemantic vehicle intentions, comprising: obtaining sensor dataassociated with a surrounding environment of a first vehicle, whereinthe sensor data comprises a sequence of image frames respectivelycorresponding to one of a plurality of time steps; determining one ormore regions of interest associated with the sensor data; determiningone or more spatial features associated with at least one of the one ormore regions of interest, wherein at least one of the one or morespatial features are indicative of a vehicle orientation associated witha second vehicle; determining one or more temporal features associatedwith at least one of the one or more regions of interest, wherein theone or more temporal features are indicative of one or more semanticstates associated with at least one signal light of the second vehicle;determining, using one or more machine learned models, an intentionindicative of a predicted future action of the second vehicle based onthe one or more spatial features and the one or more temporal features,wherein the one or more spatial features and the one or more temporalfeatures are input into at least one of the one or more machine learnedmodels; and initiating one or more actions based on the intention. 2.The computer-implemented method of claim 1, wherein the one or moreregions of interest comprise one or more cropped image frames associatedwith the second vehicle.
 3. The computer-implemented method of claim 2,wherein the one or more cropped image frames comprise data indicative ofthe at least one signal light of the second vehicle.
 4. Thecomputer-implemented method of claim 1, wherein at least one of the oneor more spatial features are indicative of a model representation of thesecond vehicle.
 5. The computer-implemented method of claim 4, whereinthe model representation of the second vehicle is indicative of avehicle orientation associated with the second vehicle.
 6. Thecomputer-implemented method of claim 5, wherein the vehicle orientationassociated with the second vehicle is relative to the first vehicle. 7.The computer-implemented method of claim 1, wherein at least one of theone or more spatial features are indicative of one or more occludingobjects.
 8. The computer-implemented method of claim 7, wherein the oneor more occluding objects comprise one or more objects disrupting a viewof the second vehicle from the first vehicle.
 9. Thecomputer-implemented method of claim 1, wherein the one or more actionscomprise providing one or more informational prompts to an operator ofthe first vehicle.
 10. A computing system comprising: one or moreprocessors; and one or more tangible, non-transitory, computer readablemedia that store instructions that when executed by the one or moreprocessors cause the computing system to perform operations comprising:obtaining sensor data associated with a surrounding environment of afirst vehicle; determining, via one or more machine learning models, oneor more regions of interest associated with the sensor data;determining, via the one or more machine learning models, one or morespatial features associated with at least one of the one or more regionsof interest, wherein at least one of the one or more spatial featuresare indicative of an object orientation associated with an object ofinterest; determining, via the one or more machine learning models, oneor more temporal features associated with at least one of the one ormore regions of interest, wherein the one or more temporal features areindicative of one or more semantic states associated with at least onesignal of the object of interest; determining, via the one or moremachine learning models, an intention indicative of a predicted futureaction of the object of interest based on the one or more spatialfeatures and the one or more temporal features; and initiating one ormore actions based on the intention.
 11. The computing system of claim10, wherein the sensor data comprises a sequence of image frames at aplurality of time steps.
 12. The computing system of claim 11, whereindetermining the one or more regions of interest comprises inputting thesequence of image frames into at least one of the one or more machinelearning models.
 13. The computing system of claim 10, whereindetermining the one or more spatial features comprises inputting the oneor more regions of interest into at least one of the one or more machinelearning models.
 14. The computing system of claim 10, whereindetermining the one or more temporal features comprises inputting aseries of the one or more regions of interest into at least one of theone or more machine learning models.
 15. The computing system of claim10, wherein determining the intention associated with the object ofinterest comprises inputting the one or more spatial features and theone or more temporal features into at least one of the one or moremachine learning models.
 16. The computing system of claim 10, whereinthe regions of interest, spatial features, temporal features, and objectintention are respectively determined separately by a different model ofthe one or more machine learning models.
 17. An autonomous vehiclecomprising: one or more vehicle sensors; one or more processors; and oneor more tangible, non-transitory, computer readable media that storeinstructions that when executed by the one or more processors cause theone or more processors to perform operations comprising: obtaining, viathe one or more vehicle sensors, sensor data associated with asurrounding environment of the autonomous vehicle, wherein the sensordata comprises a sequence of image frames at a plurality of time steps;determining a region of interest associated with the sensor data;determining one or more spatial features associated with the one or moreregion of interest, wherein at least one of the one or more spatialfeatures are indicative of a second vehicle orientation; determining oneor more temporal features associated with the region of interest;determining, using one or more machine learned models, an intentionindicative of a predicted future action of the second vehicle based onthe one or more spatial features and the one or more temporal features,wherein the one or more spatial features and the one or more temporalfeatures are input into at least one of the one or more machine learnedmodels; and initiating one or more actions based on the intention. 18.The autonomous vehicle of claim 17, wherein the autonomous vehiclefurther comprises one or more output devices, and wherein the operationsfurther comprise: providing, via the one or more output devices, dataindicative of the intention to one or more operators of the autonomousvehicle.
 19. The autonomous vehicle of claim 17, wherein the one or moreactions comprise generating a motion plan for the autonomous vehiclebased on the intention.
 20. The autonomous vehicle of claim 19, whereinthe one or more actions further comprise initiating one or more actionsbased on the motion plan.